Effect of vitamin D combined with anti-tuberculosis drugs on serum IL-1β, IFN-γ and Th17 cell-associated cytokines for the management of spinal tuberculosis

Purpose: To investigate the effect of combination of vitamin D and anti-tuberculosis drugs on serum interleukin-1β (IL-1β), interferon-γ (IFN-γ) and helper T 17 (Th17) cell-associated cytokine levels for the treatment of spinal tuberculosis (TB). Methods: Ninety-two spinal TB patients were assigned without bias to two groups (46/group): study group (vitamin D combined with anti-TB drug group) and control group (anti-TB drug group). After treatment for 8 weeks, clinical effectiveness, adverse reactions, visual analog scale (VAS) score, spinal cord injury grade, and serum levels of IL-1β, IFN-γ, Th17, IL-10, TGF-β1, IL-17 and IL-23 were assayed with ELISA, and compared between groups. Results: Study group total effectiveness was significantly higher than that in the control group (95.65 % vs 80.43 %, p < 0.05). Before drug administration, VAS score, degree of spinal cord injury and serum levels of IL-1β, IFN-γ, IL-10, TGF-β1, IL-17 and IL-23 were comparable in the study and control patients (p > 0.05). However, post-treatment, these parameters significantly decreased in both groups (p < 0.05), but were markedly lower in study group patients, relative to controls (p < 0.05). Conclusion: The use of combined treatment of vitamin D and anti-TB drugs is an effective and safe way to alleviate inflammatory response and improve the immunity of spinal TB patients via the regulation of the levels of Th17 cell-related factors

Linking Aboveground Traits to Root Traits and Local Environment: Implications of the Plant Economics Spectrum.

The plant economics spectrum proposes that ecological traits are functionally coordinated and adapt along environmental gradients. However, empirical evidence is mixed about whether aboveground and root traits are consistently linked and which environmental factors drive functional responses. Here we measure the strength of relationships between aboveground and root traits, and examine whether community-weighted mean trait values are adapted along gradients of light and soil fertility, based on the seedling censuses of 57 species in a subtropical forest. We found that aboveground traits were good predictors of root traits; specific leaf area, dry matter, nitrogen and phosphorus content were strongly correlated with root tissue density and specific root length. Traits showed patterns of adaptation along the gradients of soil fertility and light; species with fast resource-acquisitive strategies were more strongly associated with high soil phosphorus, potassium, openness, and with low nitrogen, organic matter conditions. This demonstrates the potential to estimate belowground traits from known aboveground traits in seedling communities, and suggests that soil fertility is one of the main factors driving functional responses. Our results extend our understanding of how ecological strategies shape potential responses of plant communities to environmental change

Three dimensional shape comparison of flexible proteins using the local-diameter descriptor

<p>Abstract</p> <p>Background</p> <p>Techniques for inferring the functions of the protein by comparing their shape similarity have been receiving a lot of attention. Proteins are functional units and their shape flexibility occupies an essential role in various biological processes. Several shape descriptors have demonstrated the capability of protein shape comparison by treating them as rigid bodies. But this may give rise to an incorrect comparison of flexible protein shapes.</p> <p>Results</p> <p>We introduce an efficient approach for comparing flexible protein shapes by adapting a <it>local diameter </it>(LD) <it>descriptor</it>. The LD descriptor, developed recently to handle skeleton based shape deformations <abbrgrp><abbr bid="B1">1</abbr></abbrgrp>, is adapted in this work to capture the invariant properties of shape deformations caused by the motion of the protein backbone. Every sampled point on the protein surface is assigned a value measuring the diameter of the 3D shape in the neighborhood of that point. The LD descriptor is built in the form of a one dimensional histogram from the distribution of the diameter values. The histogram based shape representation reduces the shape comparison problem of the flexible protein to a simple distance calculation between 1D feature vectors. Experimental results indicate how the LD descriptor accurately treats the protein shape deformation. In addition, we use the LD descriptor for protein shape retrieval and compare it to the effectiveness of conventional shape descriptors. A sensitivity-specificity plot shows that the LD descriptor performs much better than the conventional shape descriptors in terms of consistency over a family of proteins and discernibility across families of different proteins.</p> <p>Conclusion</p> <p>Our study provides an effective technique for comparing the shape of flexible proteins. The experimental results demonstrate the insensitivity of the LD descriptor to protein shape deformation. The proposed method will be potentially useful for molecule retrieval with similar shapes and rapid structure retrieval for proteins. The demos and supplemental materials are available on <url>https://engineering.purdue.edu/PRECISE/LDD</url>.</p

Silver Metallization of Polyimide Surfaces Using EnvironmentallyFriendly Reducing Agents

Two environmentally friendly reducing agents, ascorbic acid and glucose, were employed to fabricate Ag-thin-film-coated polyimide(PI) films. Ascorbic acid is an acidic reducing agent, whereas glucose is an alkaline reducing agent. Both of these reducing agentsare capable of reducing Ag+ ions doped in poly(amic acid) (PAA) formed by hydrolysis of a PI surface. Only glucose can producea continuous and dense Ag thin film on a PAA surface. Granular and discontinuous Ag thin films were obtained when ascorbic acidwas employed as a reducing agent. This difference in reactivity is attributed to the pH values of these reducing solutions

Learning Consistency-Aware Unsigned Distance Functions Progressively from Raw Point Clouds

Surface reconstruction for point clouds is an important task in 3D computer vision. Most of the latest methods resolve this problem by learning signed distance functions (SDF) from point clouds, which are limited to reconstructing shapes or scenes with closed surfaces. Some other methods tried to represent shapes or scenes with open surfaces using unsigned distance functions (UDF) which are learned from large scale ground truth unsigned distances. However, the learned UDF is hard to provide smooth distance fields near the surface due to the noncontinuous character of point clouds. In this paper, we propose a novel method to learn consistency-aware unsigned distance functions directly from raw point clouds. We achieve this by learning to move 3D queries to reach the surface with a field consistency constraint, where we also enable to progressively estimate a more accurate surface. Specifically, we train a neural network to gradually infer the relationship between 3D queries and the approximated surface by searching for the moving target of queries in a dynamic way, which results in a consistent field around the surface. Meanwhile, we introduce a polygonization algorithm to extract surfaces directly from the gradient field of the learned UDF. The experimental results in surface reconstruction for synthetic and real scan data show significant improvements over the state-of-the-art under the widely used benchmarks.Comment: Accepted by NeurIPS 2022. Project page:https://junshengzhou.github.io/CAP-UDF. Code:https://github.com/junshengzhou/CAP-UD

Time-Selective RNN for Device-Free Multi-Room Human Presence Detection Using WiFi CSI

Human presence detection is a crucial technology for various applications, including home automation, security, and healthcare. While camera-based systems have traditionally been used for this purpose, they raise privacy concerns. To address this issue, recent research has explored the use of channel state information (CSI) approaches that can be extracted from commercial WiFi access points (APs) and provide detailed channel characteristics. In this thesis, we propose a device-free human presence detection system for multi-room scenarios using a time-selective conditional dual feature extract recurrent Network (TCD-FERN). Our system is designed to capture significant time features with the condition on current human features using a dynamic and static (DaS) data preprocessing technique to extract moving and spatial features of people and differentiate between line-of-sight (LoS) path blocking and non-blocking cases. To mitigate the feature attenuation problem caused by room partitions, we employ a voting scheme. We conduct evaluation and real-time experiments to demonstrate that our proposed TCD-FERN system can achieve human presence detection for multi-room scenarios using fewer commodity WiFi APs